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DNA Methylation-Based Prognostic Biomarkers of Acute Myeloid Leukemia Patients

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DNA Methylation-Based Prognostic Biomarkers of Acute Myeloid Leukemia Patients 737 Original Article Page 1 of 15 DNA methylation-based prognostic biomarkers of acute myeloid leukemia patients Linjun Hu1#, Yuling Gao2#, Zhan Shi3, Yang Liu1, Junjun Zhao4, Zunqiang Xiao3, Jiayin Lou5, Qiuran Xu6, Xiangmin Tong6 1The Medical College of Qingdao University, Qingdao 266071, China; 2Department of Genetic Laboratory, Shaoxing Women and Children Hospital, Shaoxing 312030, China; 3The Second Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou 310014, China; 4Graduate Department, Bengbu Medical College, Bengbu 233030, China; 5Department of Clinical Laboratory, Da jiang dong Hospital, Hangzhou, 310014, China; 6The Key Laboratory of Tumor Molecular Diagnosis and Individualized Medicine of Zhejiang Province, Zhejiang Provincial People’s Hospital (People’s Hospital of Hangzhou Medical College), Hangzhou 310014, China Contributions: (I) Conception and design: L Hu, Y Gao, Q Xu; (II) Administrative support: X Tong; (III) Provision of study materials or patients: Z Shi, Y Liu, J Lou ; (IV) Collection and assembly of data: J Zhao; (V) Data analysis and interpretation: Z Xiao; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. #These authors are co-first authors. Correspondence to: Qiuran Xu, MD, PhD; Xiangmin Tong, MD, PhD. Key Laboratory of Tumor Molecular Diagnosis and Individualized Medicine of Zhejiang Province, Zhejiang Provincial People’s Hospital (People’s Hospital of Hangzhou Medical College), Hangzhou 310014, China. Email: [email protected]; [email protected]. Background: Acute myeloid leukemia (AML) is a heterogeneous clonal disease that prevents normal myeloid differentiation with its common features. Its incidence increases with age and has a poor prognosis. Studies have shown that DNA methylation and abnormal gene expression are closely related to AML. Methods: The methylation array data and mRNA array data are from the Gene Expression Omnibus (GEO) database. Through the GEO data, we identified differential genes from tumors and normal samples. Then we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses on these differential genes. Protein-protein interaction (PPI) network construction and module analysis were performed to screen the highest-scoring modules. Next, we used SurvExpress software to analyze the genes in the highest-scoring module and selected potential prognostic genes by univariate and multivariate Cox analysis. Finally, the three genes screened by SurvExpress software were analyzed using the methylation analysis site MethSurv to explore AML associated methylation biomarkers. Results: We found three genes that can be used as independent prognostic factors for AML. These three genes are the low expression/methylation genes ATP11A and ITGAM, and the high expression/low methylation gene ZNRF2. Conclusions: In this study, we performed a comprehensive analysis of DNA methylation and gene expression to identify key epigenetic genes in AML. Keywords: Acute myeloid leukemia (AML); methylation; gene expression; biomarker Submitted Nov 04, 2019. Accepted for publication Nov 21, 2019. doi: 10.21037/atm.2019.11.122 View this article at: http://dx.doi.org/10.21037/atm.2019.11.122 © Annals of Translational Medicine. All rights reserved. Ann Transl Med 2019;7(23):737 | http://dx.doi.org/10.21037/atm.2019.11.122 Page 2 of 15 Hu et al. Prognostic markers of AML methylation genes Introduction and differentially expressed genes (DEGs) in AML remain unclear. This study aims to analyze acute myeloid leukemia (AML); In this study, we performed a comprehensive analysis a group of genetically heterogeneous malignant clonal of DNA methylation and gene expression to identify key diseases that can block normal myeloid differentiation (1), epigenetic genes in AML. The methylation genes and as well as AML which is also the most common type of differential genes of AML patients and normal individuals adult leukemia with the poorest prognosis. were downloaded from the GEO database. After data Only in the United States of 2019, there have been preprocessing, we identified differential genes between 10,920 deaths, and 21,450 new cases of AML diagnosed (2). tumors and normal samples and performed KEGG and At present, induction therapy for AML is based primarily GO analyses on these genes. Protein-protein interaction on cytotoxic drugs that enable complete remission (CR) in (PPI) network construction and module analysis were then 70% of adult patients (3). However, the likelihood of the performed, and the highest-scoring modules were screened. recurrence rate in AML patients is still high, especially in SurvExpress software and analyzed the genes to be assigned older patients with prognosis of having high-risk factors. the highest-scoring module with a P value <0.05 were Yet, the long-term cure rate was lower in the absence of selected to perform survival analysis and risk assessment allogeneic hematopoietic stem cell transplantation (Allo- in the cancer dataset. Finally, MethSurv analyzed the HCT) in adult AML patients with first complete remission three genes screened by SurvExpress software to explore (CR1) (4). methylation biomarkers associated with AML survival. With the emergence of various molecular targeted therapies for AML in the last two years, there are now several new drugs and other clinical trials currently Methods underway; however, there is not enough data to know Microarray data which newly approved drugs should be chosen or in which order or combination they should be used in (2). New We extracted gene expression (GSE114868) and early diagnostic biomarkers and therapeutic targets are methylation (GSE63409) profiling data from the Gene therefore urgently needed to improve the diagnosis and risk Expression Omnibus (GEO) database at the National management of AML in order to reduce the mortality rate Center for Biotechnology Information. of AML. The AML-associated dataset GSE63409 submitted by Epigenetic modifications include a class of heritable Jung N based on the GPL13534 platform was obtained non-genetic changes in gene expression, usually including from the GEO database and included 15 AML samples and DNA methylation, histone modifications, and chromatin 5 normal samples. The AML-associated dataset GSE114868 remodeling (5). In healthy hematopoietic stem cells, submitted by Huang H based on the GPL17586 platform epigenetic processes play a key role in cell differentiation was obtained from the GEO database and included 194 and hematopoiesis (6). Among them, DNA methylation AML samples and 20 normal samples (Figure 1). affects the function of key genes. It is closely related to tumors by silencing tumor suppressor genes and activating Identification of DEGs oncogenes by high/low methylation (7). Abnormal DNA methylation is considered a hallmark of AML and is GEO provides users with a useful tool called GEO2R that considered a powerful epigenetic marker in early diagnosis, can be used to analyze microarray data. GRO2R (https:// prognosis prediction, and treatment decision making (8). www.ncbi.nlm.nih.gov/geo/geo2r/) were used to analyze Abnormal gene expression is closely related to tumor gene expression in GSE114868 and GSE63409. Gene prognosis. Studies have shown that NPM1 (9), FLT3 expression (GSE114868) and methylation (GSE63409) (10,11), C-KIT (12), AML1-ETO (13), RUNX1 (3,14), TP53 data set genes were considered statistically significant as |t| (3,15), CBFB/MYH11 (13,16), TET2 (17), DNMT3A (18), ≥2.0 and adj P<0.01. Then, we identified hypomethylated/ JAK-STAT (19), and CXCR4 (20,21) ,HOXA family (22), upregulated genes via overlapping the hypomethylated and NAT10 (23) gene are associated with prognosis of AML. A overexpression gene lists and identified hypermethylated/ few studies have shown altered DNA methylation in cancer, downregulated genes via overlapping the hypermethylated but the roles of key differentially methylated genes (DMGs) and low-expression gene lists. Finally, Then, we use the © Annals of Translational Medicine. All rights reserved. Ann Transl Med 2019;7(23):737 | http://dx.doi.org/10.21037/atm.2019.11.122 Annals of Translational Medicine, Vol 7, No 23 December 2019 Page 3 of 15 GEO database 1. AML and DNA methylation 2. Homo sapiens 3. Array Datasets incorporating AML and DNA methylation subjects GEO2R DMGs and DEGs identification 1. AML and DNA methylation 2. Homo sapiens 3. Array KEGG pathway GO pathway Highest score module enrichment analysis enrichment analysis genetic analysis SurvExpress Screen out 3 independent prognostic factors ROC MethSurv Establish a prognostic Methylation model biomarkers Figure 1 Graphical abstract. Venn diagram network tool to draw the Venn diagram. Then, MCODE plugin in Cytoscape is used to find clusters (http://bioinformatics.psb.ugent.be/webtools/Venn/). with the degree cut-off, haircut on, k-core, node score cut- off, max depth set as 10, 0.2, 2, 0.2 and 100 in PPI network and the module with the highest score would be picked out Functional enrichment analyses for module genes to make survival analyses. The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a database resource for understanding high-level functions Survival analysis and Cox regression, ROC curve and utilities of the biological system from molecular-level information. The Gene Ontology (GO) could be used to SurvExpress is a comprehensive gene expression database perform enrichment analysis. We used DAVID (https:// and web-based tool that uses biomarker gene lists as david.ncifcrf.gov/)
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